{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8b0e11f2-9ea4-48c2-b8d2-d0a4ba967827",
   "metadata": {},
   "source": [
    "# Gradio Day!\n",
    "\n",
    "Today we will build User Interfaces using the outrageously simple Gradio framework.\n",
    "\n",
    "Prepare for joy!\n",
    "\n",
    "Please note: your Gradio screens may appear in 'dark mode' or 'light mode' depending on your computer settings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c44c5494-950d-4d2f-8d4f-b87b57c5b330",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "\n",
    "import os\n",
    "from dotenv import load_dotenv\n",
    "from openai import OpenAI\n",
    "import gradio as gr\n",
    "import requests\n",
    "from bs4 import BeautifulSoup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "337d5dfc-0181-4e3b-8ab9-e78e0c3f657b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load environment variables in a file called .env\n",
    "# Print the key prefixes to help with any debugging\n",
    "\n",
    "load_dotenv(override=True)\n",
    "openai_api_key = os.getenv('OPENAI_API_KEY')\n",
    "anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n",
    "google_api_key = os.getenv('GOOGLE_API_KEY')\n",
    "\n",
    "if openai_api_key:\n",
    "    print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n",
    "else:\n",
    "    print(\"OpenAI API Key not set\")\n",
    "    \n",
    "if anthropic_api_key:\n",
    "    print(f\"Anthropic API Key exists and begins {anthropic_api_key[:7]}\")\n",
    "else:\n",
    "    print(\"Anthropic API Key not set\")\n",
    "\n",
    "if google_api_key:\n",
    "    print(f\"Google API Key exists and begins {google_api_key[:8]}\")\n",
    "else:\n",
    "    print(\"Google API Key not set\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "010ba7ae-7b74-44fc-b1b0-d21860588093",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set base url\n",
    "\n",
    "ANTHROPIC_BASE_URL = \"https://api.anthropic.com/v1/\"\n",
    "GEMINI_BASE_URL = \"https://generativelanguage.googleapis.com/v1beta/openai/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22586021-1795-4929-8079-63f5bb4edd4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Connect to OpenAI, Anthropic and Google; comment out the Claude or Google lines if you're not using them\n",
    "\n",
    "openai = OpenAI()\n",
    "\n",
    "claude = OpenAI(base_url=ANTHROPIC_BASE_URL, api_key=anthropic_api_key)\n",
    "\n",
    "gemini = OpenAI(base_url=GEMINI_BASE_URL, api_key=google_api_key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3895dde-3d02-4807-9e86-5a3eb48c5260",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set models\n",
    "\n",
    "gpt_model = \"gpt-4.1-mini\"\n",
    "claude_model = \"claude-3-5-haiku-latest\"\n",
    "gemini_model = \"gemini-2.0-flash\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af9a3262-e626-4e4b-80b0-aca152405e63",
   "metadata": {},
   "outputs": [],
   "source": [
    "system_message = \"You are a helpful assistant that responds in markdown\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88c04ebf-0671-4fea-95c9-bc1565d4bb4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def stream_gpt(prompt):\n",
    "    messages = [\n",
    "        {\"role\": \"system\", \"content\": system_message},\n",
    "        {\"role\": \"user\", \"content\": prompt}\n",
    "      ]\n",
    "    stream = openai.chat.completions.create(\n",
    "        model=gpt_model,\n",
    "        messages=messages,\n",
    "        stream=True\n",
    "    )\n",
    "    result = \"\"\n",
    "    for chunk in stream:\n",
    "        result += chunk.choices[0].delta.content or \"\"\n",
    "        yield result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "901256fd-675c-432d-bd6e-49ab8dade125",
   "metadata": {},
   "outputs": [],
   "source": [
    "def stream_claude(prompt):\n",
    "    messages = [\n",
    "        {\"role\": \"system\", \"content\": system_message},\n",
    "        {\"role\": \"user\", \"content\": prompt}\n",
    "      ]\n",
    "    stream = claude.chat.completions.create(\n",
    "        model=claude_model,\n",
    "        messages=messages,\n",
    "        max_tokens=1000,\n",
    "        stream=True\n",
    "    )\n",
    "    result = \"\"\n",
    "    for chunk in stream:\n",
    "        result += chunk.choices[0].delta.content or \"\"\n",
    "        yield result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d7e0d48-6140-484c-81aa-2f6aa6da8f25",
   "metadata": {},
   "outputs": [],
   "source": [
    "def stream_gemini(prompt):\n",
    "    messages = [\n",
    "        {\"role\": \"system\", \"content\": system_message},\n",
    "        {\"role\": \"user\", \"content\": prompt}\n",
    "      ]\n",
    "    stream = gemini.chat.completions.create(\n",
    "        model=gemini_model,\n",
    "        messages=messages,\n",
    "        stream=True\n",
    "    )\n",
    "    result = \"\"\n",
    "    for chunk in stream:\n",
    "        result += chunk.choices[0].delta.content or \"\"\n",
    "        yield result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0087623a-4e31-470b-b2e6-d8d16fc7bcf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def stream_model(prompt, model):\n",
    "    if model==\"GPT\":\n",
    "        result = stream_gpt(prompt)\n",
    "    elif model==\"Claude\":\n",
    "        result = stream_claude(prompt)\n",
    "    elif model==\"Gemini\":\n",
    "        result = stream_gemini(prompt)\n",
    "    else:\n",
    "        raise ValueError(\"Unknown model\")\n",
    "    yield from result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d8ce810-997c-4b6a-bc4f-1fc847ac8855",
   "metadata": {},
   "outputs": [],
   "source": [
    "view = gr.Interface(\n",
    "    fn=stream_model,\n",
    "    inputs=[gr.Textbox(label=\"Your message:\"), gr.Dropdown([\"GPT\", \"Claude\", \"Gemini\"], label=\"Select model\", value=\"GPT\")],\n",
    "    outputs=[gr.Markdown(label=\"Response:\")],\n",
    "    flagging_mode=\"never\"\n",
    ")\n",
    "view.launch()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d933865b-654c-4b92-aa45-cf389f1eda3d",
   "metadata": {},
   "source": [
    "# Building a company brochure generator\n",
    "\n",
    "Now you know how - it's simple!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1626eb2e-eee8-4183-bda5-1591b58ae3cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# A class to represent a Webpage\n",
    "\n",
    "class Website:\n",
    "    url: str\n",
    "    title: str\n",
    "    text: str\n",
    "\n",
    "    def __init__(self, url):\n",
    "        self.url = url\n",
    "        response = requests.get(url)\n",
    "        self.body = response.content\n",
    "        soup = BeautifulSoup(self.body, 'html.parser')\n",
    "        self.title = soup.title.string if soup.title else \"No title found\"\n",
    "        for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n",
    "            irrelevant.decompose()\n",
    "        self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n",
    "\n",
    "    def get_contents(self):\n",
    "        return f\"Webpage Title:\\n{self.title}\\nWebpage Contents:\\n{self.text}\\n\\n\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c701ec17-ecd5-4000-9f68-34634c8ed49d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# With massive thanks to Bill G. who noticed that a prior version of this had a bug! Now fixed.\n",
    "\n",
    "base_system_message = \"You are an assistant that analyzes the contents of a company website landing page \\\n",
    "and creates a short brochure about the company for prospective customers, investors and recruits. Respond in markdown.\"\n",
    "system_message = base_system_message"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11e9debb-9500-4783-a72e-fc3659214a8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def system_personality(personality) -> str:\n",
    "    match personality:\n",
    "        case \"Hostile\":\n",
    "            return base_system_message + \" Use a critical and sarcastic tone that highlights flaws, inconsistencies, or poor design choices in the company's website.\"\n",
    "        case \"Formal\":\n",
    "            return base_system_message + \" Use a professional and respectful tone, with precise language and a structured presentation that inspires trust.\"\n",
    "        case \"Funny\":\n",
    "            return base_system_message + \" Use a lighthearted and humorous tone, incorporating playful language, witty remarks and engaging expressions.\"\n",
    "        case _:\n",
    "            return base_system_message"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5def90e0-4343-4f58-9d4a-0e36e445efa4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def stream_brochure(company_name, url, model, personality):\n",
    "    yield \"\"\n",
    "    prompt = f\"Please generate a company brochure for {company_name}. Here is their landing page:\\n\"\n",
    "    prompt += Website(url).get_contents()\n",
    "    global system_message\n",
    "    system_message = system_personality(personality)\n",
    "    if model==\"GPT\":\n",
    "        result = stream_gpt(prompt)\n",
    "    elif model==\"Claude\":\n",
    "        result = stream_claude(prompt)\n",
    "    elif model==\"Gemini\":\n",
    "        result = stream_gemini(prompt)\n",
    "    else:\n",
    "        raise ValueError(\"Unknown model\")\n",
    "    yield from result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66399365-5d67-4984-9d47-93ed26c0bd3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "view = gr.Interface(\n",
    "    fn=stream_brochure,\n",
    "    inputs=[\n",
    "        gr.Textbox(label=\"Company name:\"),\n",
    "        gr.Textbox(label=\"Landing page URL including http:// or https://\"),\n",
    "        gr.Dropdown([\"GPT\", \"Claude\", \"Gemini\"], label=\"Select model\"),\n",
    "        gr.Dropdown([\"Funny\",\"Formal\", \"Hostile\"], label=\"Select a personality\")],\n",
    "    outputs=[gr.Markdown(label=\"Brochure:\")],\n",
    "    flagging_mode=\"never\"\n",
    ")\n",
    "view.launch()"
   ]
  }
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